Business owners examine many variables when assessing the advantages and disadvantages associated with finding qualified people to fill its workforce. Whether a business is relocating, building in a new area, expanding, or diversifying its workforce, poor analysis of industry and occupational variables frustrates a business's ability to make informed decisions. The cost and risk of making the wrong decision as to which geographic areas to explore, relative to workforce decisions, are significant. When multiple target areas are selected for investment or further research, thousands, even hundreds of thousands of dollars, and weeks, even months or years, are at risk if even one critical variable does not meet its threshold value.
The industry and occupational variables may include without limitation: the size of a metropolitan area, the number and types of minorities within an occupation, occupation employment, occupation unemployment, occupation wage, standard of living, industry unemployment, industry employment, total employment for an area, total unemployment for an area, job creation, new hires in occupations and industry, separations, turnover rate, average wages, cost of living, average wage inverse, unemployment, unemployment average, industry workforce, percentage of minorities, gender make up of an area, and the like. All variables are analyzed with respect to a user's prioritized needs and interest in having a business in a certain geographic area.
Various agencies collect data helpful to locating workers, non-limiting examples being the U.S. Census Bureau, the U.S. Bureau of Labor and Statistics, Bureau of Economic Analysis, America's Labor Market Information System, Council for Community and Economic Research, other private agencies, and the like. However, their databases are decentralized and somewhat chaotic. Each agency's system is designed for a different purpose, and, as a result, they often measure the same variable differently. Therefore, they arrive at results that may confuse non-economists. For example, the U.S. Census Bureau contains statistical data as related to industry while the Bureau of Labor and Statistics contains statistical data relating to occupations within industries.
Analyzers study and develop findings and conclusions for each database individually, but it is difficult to compare the data from each database on an “apples to apples” basis. None of these methods use a normalization variable or truly integrate the data from the databases into a coherent relationship to quickly analyze an employer's needs when filing its workforce.
Other methods include an in-depth and costly study of variables within a selected set of Metropolitan Statistical Areas, or MSAs. The drawbacks to these methods include selecting a bad area to either relocate or start your business, where the area is unattractive because of basic demographic, geographic, or economic variables that were difficult to identify prior to doing a more in depth study. It is important for businesses to eliminate all of the bad choices before paying a quarter million dollars to do a feasibility study on entering a specific geographic region to do business.
Therefore, there is a need for a computer-implemented method and system that allows a user to access and analyze worker demographics which are critical to making workforce decisions between and amongst geographic areas. The present invention allows the user to select critical factors relating to worker demographics, extract and rank all area's returning data based on an area's lowest index number, screening areas with unacceptable low values, and weighting the ranked critical factors for each area based on the user's needs, which measures the utility of each area. The user's informed decision is based on the present invention's ability to screen those areas with unacceptable low values and have the most potential for a high return on workforce needs.